Publications

A Controller Recognizer Framework: How necessary is recognition for control?

Recently there has been growing interest in building active visual object recognizers, as opposed to the usual passive recognizers which cla… (voir plus)ssifies a given static image into a predefined set of object categories. In this paper we propose to generalize these recently proposed end-to-end active visual recognizers into a controller-recognizer framework. A model in the controller-recognizer framework consists of a controller, which interfaces with an external manipulator, and a recognizer which classifies the visual input adjusted by the manipulator. We describe two most recently proposed controller-recognizer models: recurrent attention model and spatial transformer network as representative examples of controller-recognizer models. Based on this description we observe that most existing end-to-end controller-recognizers tightly, or completely, couple a controller and recognizer. We ask a question whether this tight coupling is necessary, and try to answer this empirically by building a controller-recognizer model with a decoupled controller and recognizer. Our experiments revealed that it is not always necessary to tightly couple them and that by decoupling a controller and recognizer, there is a possibility of building a generic controller that is pretrained and works together with any subsequent recognizer.

2016-02-17

ICLR.cc/2016/workshop (inconnu)

openreview.net

Task Loss Estimation for Sequence Prediction

Nan Rosemary Ke

2016-02-17

(publié)

openreview.net

Variance Reduction in SGD by Distributed Importance Sampling

Humans are able to accelerate their learning by selecting training materials that are the most informative and at the appropriate level of d… (voir plus)ifficulty. We propose a framework for distributing deep learning in which one set of workers search for the most informative examples in parallel while a single worker updates the model on examples selected by importance sampling. This leads the model to update using an unbiased estimate of the gradient which also has minimum variance when the sampling proposal is proportional to the L2-norm of the gradient. We show experimentally that this method reduces gradient variance even in a context where the cost of synchronization across machines cannot be ignored, and where the factors for importance sampling are not updated instantly across the training set.

2016-02-16

ICLR.cc/2016/workshop (inconnu)

openreview.net

Fault-Tolerant Associative Memories Based on $c$-Partite Graphs

François Leduc-Primeau

Vincent Gripon

Michael G. Rabbat

Warren J. Gross

Associative memories allow the retrieval of previously stored messages given a part of their content. In this paper, we are interested in as… (voir plus)sociative memories based on c-partite graphs that were recently introduced. These memories are almost optimal in terms of the amount of storage they require (efficiency) and allow retrieving messages with low complexity. We propose a generic implementation model for the retrieval algorithm that can be readily mapped to an integrated circuit and study the retrieval performance when hardware components are affected by faults. We show using analytical and simulation results that these associative memories can be made resilient to circuit faults with a minor modification of the retrieval algorithm. In one example, the memory retains 88% of its efficiency when 1% of the storage cells are faulty, or 98% when 0.1% of the binary outputs of the retrieval algorithm are faulty. When considering storage faults, the fault tolerance exhibited by the proposed associative memory can be comparable to using a capacity-achieving error correction code for protecting the stored information.

2016-02-14

IEEE Transactions on Signal Processing (publié)

doi.org

Discriminative Regularization for Generative Models

Alex Lamb

Vincent Dumoulin

Aaron Courville

We explore the question of whether the representations learned by classifiers can be used to enhance the quality of generative models. Our c… (voir plus)onjecture is that labels correspond to characteristics of natural data which are most salient to humans: identity in faces, objects in images, and utterances in speech. We propose to take advantage of this by using the representations from discriminative classifiers to augment the objective function corresponding to a generative model. In particular we enhance the objective function of the variational autoencoder, a popular generative model, with a discriminative regularization term. We show that enhancing the objective function in this way leads to samples that are clearer and have higher visual quality than the samples from the standard variational autoencoders.

2016-02-08

ArXiv (prépublication)

arxiv.org

Deep Learning Vector Quantization

Harm de Vries

Roland Memisevic

Aaron Courville

. While deep neural nets (DNN’s) achieve impressive performance on image recognition tasks, previous studies have reported that DNN’s gi… (voir plus)ve high conﬁdence predictions for unrecognizable images. Motivated by the observation that such fooling examples might be caused by the extrapolating nature of the log-softmax, we propose to combine neural networks with Learning Vector Quantization (LVQ). Our proposed method, called Deep LVQ (DLVQ), achieves comparable performance on MNIST while being more robust against fooling and adversarial examples.

2015-12-31

The European Symposium on Artificial Neural Networks (publié)

dblp.uni-trier.de

Editorial on Special Issue on Probabilistic Models for Biomedical Image Analysis.

Tal Arbel

Manuel Jorge Cardoso

William M. Wells III

Albert C. S. Chung

Doina Precup

2015-12-31

Computer Vision and Image Understanding (publié)

doi.org

Former NASA chief unveils $ 100 million neural chip maker KnuEdge

C. Strasser

Dean Takahashi

Tim Klinger

Gerald Tesauro

Kartik Talamadupula

Bowen Zhou

Yoshua Bengio

Aaron Courville

Medium, Moore Data, Carly Strasser from June 07, 2016 Open access to research articles has been in the news quite a bit lately (see the SciH… (voir plus)ub controversy, the preprints in biology discussion, and the European Union’s recent announcement). The Data-Driven Discovery team at the Moore Foundation has also been discussing open access, particularly as it relates to the publications generated by our #MooreData researchers. Our grantee population is fairly progressive when it comes to open science, and many of the outputs that they generate are already publicly available (including proposals, software, workflows, and publications). It is therefore easy for us to imagine that they would embrace a policy that mandates open access for research articles that they produce. That said, we are always open to discussions!

2015-12-31

(publié)

www.semanticscholar.org

Hierarchical Memory Networks

Gerald Tesauro

Memory networks are neural networks with an explicit memory component that can be both read and written to by the network. The memory is oft… (voir plus)en addressed in a soft way using a softmax function, making end-to-end training with backpropagation possible. However, this is not computationally scalable for applications which require the network to read from extremely large memories. On the other hand, it is well known that hard attention mechanisms based on reinforcement learning are challenging to train successfully. In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks. The memory is organized in a hierarchical structure such that reading from it is done with less computation than soft attention over a flat memory, while also being easier to train than hard attention over a flat memory. Specifically, we propose to incorporate Maximum Inner Product Search (MIPS) in the training and inference procedures for our hierarchical memory network. We explore the use of various state-of-the art approximate MIPS techniques and report results on SimpleQuestions, a challenging large scale factoid question answering task.

2015-12-31

CoRR (publié)

doi.org

arxiv.org

Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging

Henning Müller

B. Kelm

Tal Arbel

Weidong (Tom) Cai

M. Jorge Cardoso

Georg Langs

Bjoern Menze

Dimitris N. Metaxas

Albert A. Montillo

William Wells

Shaoting Zhang

Albert C.S. Chung

M. Jenkinson

Annemie Ribbens

2015-12-31

Lecture Notes in Computer Science (publié)

doi.org

Professor Forcing: A New Algorithm for Training Recurrent Networks

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the networ… (voir plus)k’s own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling from the network over multiple time steps. We apply Professor Forcing to language modeling, vocal synthesis on raw waveforms, handwriting generation, and image generation. Empirically we find that Professor Forcing acts as a regularizer, improving test likelihood on character level Penn Treebank and sequential MNIST. We also find that the model qualitatively improves samples, especially when sampling for a large number of time steps. This is supported by human evaluation of sample quality. Trade-offs between Professor Forcing and Scheduled Sampling are discussed. We produce T-SNEs showing that Professor Forcing successfully makes the dynamics of the network during training and sampling more similar.

2015-12-31

Advances in Neural Information Processing Systems 29 (NIPS 2016) (publié)

doi.org

arxiv.org

Theano: A Python framework for fast computation of mathematical expressions

The Theano Development Team

Rami Al-Rfou

Guillaume Alain

Amjad Almahairi

Christof Angermueller

Dzmitry Bahdanau

Nicolas Ballas

Frédéric Bastien

Justin Bayer

Anatoly Belikov

Alexander Belopolsky

Josh Bleecher Snyder

Nicolas Boulanger-Lewandowski

Xavier Bouthillier

Alexandre De Brébisson … (voir 93 de plus)

Pierre-Luc Carrier

Paul Christiano

Myriam Côté

Yann N. Dauphin

Julien Demouth

Sander Dieleman

Samira Ebrahimi Kahou

Ziye Fan

Mathieu Germain

Matt Graham

Balázs Hidasi

Arjun Jain

Kai Jia

Mikhail Korobov

Vivek Kulkarni

Alex Lamb

Pascal Lamblin

Eric Larsen

César Laurent

Sean Lee

Simon Lefrancois

Simon Lemieux

Nicholas Léonard

Zhouhan Lin

Jesse A. Livezey

Cory Lorenz

Jeremiah Lowin

Qianli Ma

Pierre-Antoine Manzagol

Robert T. McGibbon

Mehdi Mirza

Alberto Orlandi

Christopher Pal

Razvan Pascanu

Mohammad Pezeshki

Colin Raffel

Daniel Renshaw

Matthew Rocklin

Adriana Romero

Markus Roth

Peter Sadowski

John Salvatier

François Savard

Jan Schlüter

John Schulman

Gabriel Schwartz

Iulian Vlad Serban

Dmitriy Serdyuk

Samira Shabanian

Etienne Simon

Sigurd Spieckermann

S. Ramana Subramanyam

Gijs van Tulder

Sebastian Urban

Dustin J. Webb

Matthew Willson

Lijun Xue

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (voir plus)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.

2015-12-31

Computing Research Repository (publié)

doi.org

arxiv.org

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